System and method for developing a common inquiry response

ABSTRACT

The present application includes a method and system for developing a common inquiry response. The system receives at least one customer contact formed by an inquiry and its response, analyzes the customer contact to determine the content of the inquiry and the response, and stores the inquiry and the response in a corresponding inquiry-response sub-database in an inquiry-response database. After analyzing at least one of the sub-databases, the system assigns a common inquiry-response (CIR) knowledge document to that inquiry-response sub-database for future use involving similar inquiries and responses. This allows a user to respond more quickly to inquiries with a reduced risk of incorrect or inconsistent information in the response.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority of U.S. Provisional ApplicationNo. 63/091,058, filed Oct. 13, 2020, the content of which isincorporated herein by reference in its entirety.

BACKGROUND

The present disclosure is directed to a method for computer analysis,specifically a method for analyzing and developing responses to commoncustomer inquiries.

In a modern high-volume customer engagement center (CEC), much of thework performed by customer service representatives (CSRs) is repetitivein nature. The most common customer requests for information may havesimple answers, but due to either the volume of contacts or theprocedures required for each contact, they take up a large percentage ofa CSR's time. During times with a large volume of contact, customerswith simple questions may need to wait for extended periods to receiverelatively simple answers. In turn, customers with complicated issuesmust wait for a CSR to finish with the large volume of simple questions,leading to frustration on the customers' part. In the event of anemergency, a significant influx of calls, emails, text messages, websitevisits, and chats may all be requesting statuses related to a singlesudden event, which may occur during a period of limited staffing.

Questions from different modes of contact such as, but not limited to,email and telephone, may not have standardized responses, leading toconfusion if one answer is given over the phone and another in an email.While some systems offer the option to read or listen to responses tofrequently asked questions (FAQs), the FAQs are determinedretroactively. Formulation of such FAQs also relies on the CSR correctlyrecalling the numbers and types of questions asked, and takes time thatmay be spent more productively. Further, accessing the FAQs requires theCSR to manually and subjectively search for the common response to theinquiry (which may or may not exist in the FAQ). Additionally, changesin the most frequent inquiries due to, for example, a change in afinancial institution's standard procedures, may be slow to update inthe FAQs, leading to more confusion.

There is an unmet need in the art for a system and method capable ofautomatically developing a common inquiry response from a group ofinquiries and responses, and then automatically applying the response tooutgoing communications.

SUMMARY

An exemplary embodiment of the present application is a method fordeveloping a common inquiry response using a dynamic analysis (DA)system in a customer engagement center (CEC) system. The method includesproviding the DA system in the CEC system, wherein the DA systemincludes at least one DA desktop, at least one speech/text analyticsservice (STAS) unit, an inquiry-response database, and a dynamicanalysis engine (DAE). The DAE is operatively connected to the at leastone DA desktop, the at least one STAS unit, and the inquiry-responsedatabase. The method receives at least one customer contact into the atleast one DA desktop, the customer contact comprising an inquiry and aresponse to the inquiry. The method then analyzes the customer contactin the at least one STAS unit according to at least one contactanalytics rule to determine the content of the inquiry and the response.Next, the method analyzes the content of the inquiry and the responsewith the DAE to calculate a similarity score from the inquiry and theresponse. The method then stores the inquiry and the response in acorresponding inquiry-response sub-database in the inquiry-responsedatabase based upon the similarity score. Next, the method analyzes atleast one of the inquiry-response sub-databases in the DAE according toat least one inquiry analytics rule to determine the common response forthat inquiry-response sub-database. The method then assigns at least onecommon inquiry-response (CIR) knowledge document to the analyzedinquiry-response sub-database.

Another exemplary embodiment of the present application is a CEC systemfor developing a common inquiry response. The CEC system includes a DAsystem. This DA system includes a DA processor and a non-transitorycomputer readable medium programmed with computer readable code thatupon execution by the DA processor causes the DA processor to executethe above-mentioned method for developing a common inquiry response.

Another exemplary embodiment of the present application is anon-transitory computer readable medium programmed with computerreadable code that upon execution by a DA processor causes the DAprocessor to execute the above-mentioned method for developing a commoninquiry response on a CEC system.

The objects and advantages will appear more fully from the followingdetailed description made in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWING(S)

FIG. 1 depicts an exemplary embodiment of a dynamic analysis system fordeveloping a common inquiry response.

FIGS. 2a, 2b, 2c, and 2d depict a flowchart of an exemplary embodimentof a method for developing a common inquiry response and utilizing sucha response in real time.

FIG. 3 depicts an exemplary embodiment of a computer system fordeveloping a common inquiry response.

DETAILED DESCRIPTION

In the present description, certain terms have been used for brevity,clearness and understanding. No unnecessary limitations are to beapplied therefrom beyond the requirement of the prior art because suchterms are used for descriptive purposes only and are intended to bebroadly construed. The different systems and methods described hereinmay be used alone or in combination with other systems and methods.Various equivalents, alternatives and modifications are possible withinthe scope of the appended claims. Each limitation in the appended claimsis intended to invoke interpretation under 35 U.S.C. § 112, sixthparagraph, only if the terms “means for” or “step for” are explicitlyrecited in the respective limitation.

Dynamic analysis (DA) systems allow ongoing analysis of contacts withcustomers on CEC systems. By analyzing inquiries going through the CECsystem in real and/or delayed time using a linked DA system, the DAsystem can allow an organization to achieve several key benefits. First,the DA system can be integrated with existing CEC systems, allowing CSRsto continue using current, familiar CEC systems. Second, the DA systemwill automatically update the preexisting knowledge base to account forchanging inquiries, thereby ensuring consistent, constantly updated CSRscripts and templates, and reduced backlogs and customerdissatisfaction. Third, the system can prompt the CSR to use a templatethat they may not have previously known about. Fourth, the DA system candetect and develop responses for low-volume inquiries that arenonetheless highly amenable to template responses. Fifth, in the eventthat a CSR goes off-script from the standard response, the system canstop the non-compliant response from being delivered and/or alert humanresources (HR) or supervisory staff that the CSR is potentiallyproviding incorrect communications to a customer.

FIG. 1 depicts an exemplary embodiment of a DA system 100 for developinga common inquiry response for use in a CEC system (not shown). The DAsystem 100 includes at least one DA desktop 110 used by a CSR forincoming and outgoing customer contacts 120, which commonly take theform of paired inquiries from customers and responses from CSRs. In theexemplary embodiment, at least one additional DA desktop 110 may be usedby HR or supervisory staff to review customer contacts 120. By way ofnon-limiting example, customer contacts 120 may include a telephoneinquiry and/or conversation between the CSR and a customer, a writteninquiry and/or communication between the CSR and a customer, a websiteupdate or other alteration, a scheduled or spontaneous post on a socialnetworking or content-sharing service, a post on a review site, a pressrelease, or any other one-way or two-way customer contact 120.

The DA system 100 includes a set of contact analytics rules 135 and aset of inquiry analytics rules 155 used to analyze all informationreceived by the DA system 100, including customer contacts 120. Thecontact analytics rules 135 may include analysis standards for verbaland/or textual communications, such as, but not limited to, keywordidentification, sentence stemming, word and punctuation standardization,metadata extraction, and intent. The inquiry analytics rules 155 mayinclude comparison standards for verbal and/or textual communications,such as, but not limited to, keyword identification, sentence stemming,and intent. The inquiry analytics rules 155 may also include standardsfor analyzing common inquiry topics, trends in inquiry topics, and CSRuse of common inquiry-response (CIR) knowledge document.

The contact and inquiry analytics rules 135 and 155 may includeexceptions and the conditions for exceptions. The contact and inquiryanalytics rules 135 and 155 may be user-generated or pre-generated, andmay be updated by users or automatically updated by DA system 100itself. The contact and inquiry analytics rules 135 and 155 may be asoftware program or programs, or a separate file or files executed by asoftware program.

The DA system 100 includes at least one speech/text analytics service(STAS) unit 130 having a STAS software module 131 and an optional STASstorage 132. In the embodiment shown, STAS storage 132 stores contactanalytics rules 135. The STAS unit 130 may be a processor or acombination of a processing system and a storage system. The STAS unit130 intercepts customer contacts 120 running through CEC desktop(s) 110and analyzes and/or standardizes customer contacts 120. The STAS unit130 uses STAS software module 131 and contact analytics rules 135 todetermine any keywords and/or intent from both the inquiry and theresponse, as well as extract metadata related to the associated customercontacts 120. The STAS unit 130 may also possess speech-to-textcapabilities to convert verbal communications to a text record. The STASunit 130 may also possess standardization capabilities to convert allcontacts to a standardized format. Optionally, STAS unit 130 may alsopermanently or temporarily save a copy of the customer contact 120 tointernal or external STAS storage 132.

The DA system 100 also includes an inquiry-response database 140containing multiple inquiry-response sub-databases 141. Each set ofrelated inquiries and their corresponding responses extracted by STASunit 130 has a separate inquiry-response sub-database 141.Inquiry-response pairs may be grouped within sub-databases 141 based onintent and/or keywords according to contact analytics rules 135.

Each sub-database 141 may also include the metadata and/or tallies ofthe number of times the inquiry-response pair occurs over a given periodof time or number of customer contacts 120. Metadata may includeinformation such as, but not limited to, sending or receiving email orIP address, a username for chat session, a telephone number, or capturedvoice data. Captured voice data may include information such as, but notlimited to, identifying data, account number, date of birth, orpassword. Metadata may be used to match specific contacts 120 with acustomer or account, match specific contacts 120 within a particularconversation to add a specific identifier to a subject line, or match acontact 120 to an open complaint or case.

The DA system 100 also includes a dynamic analysis engine (DAE) 150having a DAE software module 151 and an optional DAE storage 152. In theembodiment shown, DAE storage 152 stores inquiry analytics rules 155.The DAE 150 may be a processor or a combination of a processing systemand a storage system. The DAE 150 can dynamically update inquiryanalytics rules 155 or receive updates to inquiry analytics rules 155from DA desktop 110. The DAE 150 receives the paired inquiries and theirresponses from at least one inquiry sub-database 141 and analyzes themusing DAE software module 151 and inquiry analytics rules 155 to providethe common response for that particular sub-database 141. Such acapability may also be used to determine if a CSR has accurately relayedcompliance phrases to a customer in a response.

For each inquiry and response, the DAE 150 may utilize a patternmatching or recommender algorithm to find similar inquiries andresponses. In one embodiment, the algorithm is a context-awarecollaborative filtering algorithm. The DAE 150 may assign a similarityscore to the instant inquiry or response based on how closely it matchesexisting inquiries or responses. Inquiries and responses with similarityscores above a high threshold may be used as a CIR knowledge document160 or the basis for templates. Inquiries and responses with similarityscores below a low threshold may directed to a CSR or other user forspecial examination.

In practice, once the common response is determined, DAE 150 maygenerate or select the CIR knowledge document 160 for a new inquirybased on the similarity score. The CIR knowledge document 160 may takethe form of verbal and/or textual scripts and/or templates, trainingmaterials, informational articles, and/or any combination thereof, andmay be displayed on DA desktops 110. Once generated, each CIR knowledgedocument 160 may be updated after a set period, when DAE 150 indicates asignificant shift in the paired inquiries and their responses, uponchange to contact or inquiry analytics rules 135 or 155, or manually.

Once CIR knowledge documents 160 have been generated, DA system 100 canintercept customer contacts 120 and analyze them in real time with STASunit 130. The STAS unit 130 may use STAS software module 131 and contactanalytics rules 135 to determine any keywords and/or intent from boththe inquiry and the response. The results may then be communicated toDAE 150 for analysis to determine any applicable CIR knowledgedocument(s) 160. The DAE 150 can then automatically transmit at leastone appropriate CIR knowledge document 160 for display on DA desktop110.

As discussed above, the DA system 100 may also intercept and analyzeoutgoing customer contacts 120 to determine if they use CIR knowledgedocument 160 correctly. Responses analyzed by the DAE 150 with resultantsimilarity scores below a low threshold indicate an incorrectly used CIRknowledge document 160. Outgoing customer contacts 120 that do not useCIR knowledge document 160 correctly may be blocked, copied and flaggedfor future review, or rerouted to the DA desktop 110 of HR orsupervisory staff for review.

By way of non-limiting example, if an incoming inquiry has a similarityscore with an inquiry in the inquiry sub-database 141 above the highthreshold, the common response for the inquiry sub-database 141 will beprovided to the CSR. By way of further non-limiting example, if anoutgoing response has a similarity score with the common response in theinquiry sub-database 141 below the low threshold, the outgoing responsewill be routed to a supervisor for review of the CSR's work.

Outside of active customer communications, DA system 100 may provideanalytics to users about common inquiry topics, trends in inquirytopics, and CSR use of CIR knowledge documents 160. Based on suchanalytics, DA system 100 may recommend updates to existing templates andscripts, modifications to CSR training, or retraining CSRs toaccommodate shifts in topics subject to increased inquiry. The DA system100 may provide updates to publicly available information as well, suchas, but not limited to, website FAQ sections and prerecorded inquiryresponses on telephone lines.

FIGS. 2a through 2d depict a flowchart of an exemplary embodiment ofmethod 200 for developing and using a common inquiry response using theDA system 100 on or in the CEC system.

As shown in FIG. 2a , in step 202, the DA system receives at least onecustomer contact. The customer contact may be an email, text chat,telephone call, video call, or any other type of contact with acustomer. In certain embodiments, the customer contact may be a textualtranscription of a prior verbal or video customer contact. Suchtranscriptions may or may not include additional data related to toneand/or sentiment of the customer contact, or any additional data notincluded in the objective transcription, such as, but not limited to,metadata.

In step 204, the STAS unit analyzes the customer contact according tocontact analytics rules to determine the content of the customer inquiryand the CSR response. This step may include speech-to-text conversionand conversion of the contact to a standardized format. Analysis of theinquiry may also be used to extract metadata.

In step 206, the DAE analyzes the customer contact and, based on thecalculated similarity score, the DA system stores the inquiry and theresponse in the corresponding inquiry-response sub-database in theinquiry-response database. The DAE may use a pattern matching orrecommender algorithm for this analysis. Optionally, DA system may alsostore the inquiry and the response in the STAS unit.

In step 208, the DA system repeats steps 202 through 206 until theinquiry-response database exceeds a data volume threshold for at leastone inquiry-response sub-database. Step 208 ensures that theinquiry-response database has a volume of data to allow statisticallymeaningful analysis of at least one inquiry-response sub-database.

In step 210, the DAE analyzes at least one of the inquiry-responsesub-databases to determine the common response for that particularinquiry-response sub-database. As above, the DAE may use a patternmatching or recommender algorithm to calculate a similarity score andutilize a response from an inquiry with a similarity score above a highthreshold.

As shown in FIG. 2b , in step 212, the DAE assigns an applicable commoninquiry-response (CIR) knowledge document to the analyzedinquiry-response sub-database. Assigning the CIR knowledge document maycomprise generating the CIR knowledge document or storing an existingCIR knowledge document for later retrieval.

In optional step 214, the DAE reassigns and/or updates the CIR knowledgedocument. Reassignment may occur after a set period, when the DAEdetects a significant shift in the paired inquiries and their responsesbeyond a preset threshold, or upon change to the contact or inquiryanalytics rules, or may be made manually by a CSR or other staff.Reassignment may include updating the CIR knowledge document or storinga different CIR knowledge document. Step 214 may occur at any time aftergeneration of the CIR knowledge document.

In optional step 216, the DA system intercepts an incoming customercontact and analyzes it in real time with the STAS unit using thecontact analytics rules to determine any keywords and/or intent.

In optional step 218, the results of the analysis of step 216, thekeywords and/or intent, are communicated to the DAE for analysis usingthe inquiry analytics rules to determine if any appropriate CIRknowledge document(s) exist in the DA system based on the similarityscore between the incoming inquiry and the inquiry associated with theCIR document.

In optional step 220, the DAE automatically transmits at least oneappropriate CIR knowledge document for display on the DA desktop.

As shown in FIG. 2c , in optional step 222, the DA system intercepts anoutgoing customer contact and analyzes it in real time with the DAEusing the inquiry analytics rules to determine if the originating CSR orother author correctly used the CIR knowledge document.

In optional step 224, the DA system automatically blocks the outgoingcustomer contact based on the result of the analysis of step 222 if thesimilarity score is below a low threshold.

In optional step 226, the outgoing customer contact is copied andflagged for future review based on the result of the analysis of step222 if the similarity score is below a low threshold.

In optional step 228, the outgoing customer contact is rerouted to theDA desktop of HR or supervisory staff for review based on the result ofthe analysis of step 222 if the similarity score is below a lowthreshold.

In optional step 230, the DAE analyzes at least one of common inquirytopics, trends in inquiry topics, and CSR use of CIR knowledge documentsusing the inquiry analytics rules.

As shown in FIG. 2d , in optional step 232, the DA system recommendsupdates to existing templates and scripts, modifications to CSRtraining, or retraining CSRs based on the result of the analysis of step230.

In optional step 234, the DA system updates publicly availableinformation based on the result of the analysis of step 230.

FIG. 3 depicts an exemplary embodiment of a system 300 for developing acommon inquiry response. The system 300 may form a part of or beoperably connected to a main CEC system. System 300 is a computingsystem that includes a processing system 306, a storage system 304,software 302, a communication interface 308, and a user interface 310.Processing system 306 loads and executes software 302 from the storagesystem 304, including at least one software component 320. When executedby computing system 300, software component 320 directs the processingsystem 306 to operate as described herein in further detail inaccordance with the method 200. Computing system 300 is a specializedsystem specifically designed to perform the steps and actions necessaryto execute the method 200 for developing a common inquiry response andthe DA system 100. While some of the component options for computingsystem 300 may include components prevalent in other computing systems,computing system 300 is a specialized computing system capable ofperforming the steps and processes described herein.

Computing system 300 includes two software components 320 for performingthe functions of STAS unit 130 and DAE 150. Although computing system300 as depicted in FIG. 3 includes two software components 320 in thepresent example, it should be understood that one or more componentscould provide the same operation. Similarly, while the description asprovided herein refers to a computing system 300 and a processing system306, it is to be recognized that implementations of such systems can beperformed using one or more processors, which may be communicativelyconnected, and such implementations are considered to be within thescope of the description. It is also contemplated that these componentsof computing system 300 may be operating in a number of physicallocations.

The processing system 306 can comprise a microprocessor and othercircuitry that retrieves and executes software 302 from storage system304. Processing system 306 can be implemented within a single processingdevice but can also be distributed across multiple processing devices orsub-systems that cooperate in existing program instructions. Examples ofprocessing systems 306 include central processing units, applicationspecific processors, and logic devices, as well as any other type ofprocessing device, combinations of processing devices, or variationsthereof. While there are a number of processing devices available tocomprise the processing system 306, the processing devices used for theprocessing system 306 are particular to this system and mustspecifically be capable of performing the processing necessary toexecute method 200 and support system 100.

The storage system 304 can comprise any storage media readable byprocessing system 306, and capable of storing software 302 that is ableto meet the needs of the specific computing system 300 and execute thestorage required for method 200 and system 100. The storage system 304may include volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage of information, suchas computer readable instructions, data structures, program modules, orother data. Storage system 304 may be implemented as a single storagedevice but may also be implemented across multiple storage devices orsub-systems. Storage system 304 can further include additional elements,such a controller capable of communicating with the processing system306.

Examples of storage media include random access memory, read onlymemory, magnetic discs, optical discs, flash memory, virtual memory, andnon-virtual memory, magnetic sets, magnetic tape, magnetic disc storageor other magnetic storage devices, or any other medium which can be usedto store the desired information and that may be accessed by aninstruction execution system, as well as any combination or variationthereof, or any other type of storage medium. In some implementations,the storage media can be a non-transitory storage media. In someimplementations, at least a portion of the storage media may betransitory. Storage media may be internal or external to system 300.While many types of storage media may be incorporated into system 300,the storage media used must be specialized to the purpose of executingthe storage requirements of method 200 and system 100 as describedherein.

User interface 310 can include one or more DA desktops 110, a mouse, akeyboard, a voice input device, a touch input device for receiving agesture from a user, a motion input device for detecting non-touchgestures and other motions by a user, and other comparable input devicesand associated processing elements capable of receiving user input froma user. Output devices such as a video display or graphical display candisplay the DA desktop 110, customer contacts 120, contact analyticsrules 135, inquiry analytics rules 155, any of the CIR knowledgedocuments 160, or another interface further associated with embodimentsof the system and method as disclosed herein. Speakers, printers, hapticdevices and other types of output devices may also be included in theuser interface 310. A CSR or other staff can communicate with computingsystem 300 through the user interface 310 in order to view the DAdesktop 110 or CIR knowledge documents 160, update contact analyticsrules 135 or inquiry analytics rules 155, view, create, or modifycustomer contacts 120, or any number of other tasks the CSR or otherstaff may want to complete with computing system 300.

As described in further detail herein, computing system 300 receives andtransmits data through communication interface 308. In embodiments, thecommunication interface 308 operates to send and/or receive data, suchas, but not limited to, customer contacts 120, CIR knowledge documents160, analytics results from operations in method 200, any other set ofdata that will necessitate or assist in an interaction between acustomer and the CSR to/from other devices and/or systems to whichcomputing system 300 is communicatively connected, and to receive andprocess customer interactions as described in greater detail above. Suchdata can include input or updates related to contact or inquiryanalytics rules 135 or 155, or updates to DA system 100 or the method200.

In the foregoing description, certain terms have been used for brevity,clearness, and understanding. No unnecessary limitations are to beinferred therefrom beyond the requirement of the prior art because suchterms are used for descriptive purposes and are intended to be broadlyconstrued. The different configurations, systems, and method stepsdescribed herein may be used alone or in combination with otherconfigurations, systems and method steps. It is to be expected thatvarious equivalents, alternatives and modifications are possible withinthe scope of the appended claims.

What is claimed is:
 1. A method for developing a common inquiry responseon a customer engagement center (CEC) system using a dynamic analysis(DA) system, comprising: providing the DA system on the CEC system,wherein the DA system includes at least one DA desktop, at least onespeech/text analytics service (STAS) unit, an inquiry-response database,a dynamic analysis engine (DAE), wherein the DAE is operativelyconnected to the at least one DA desktop, the at least one STAS unit,and the inquiry-response database; receiving at least one customercontact into the at least one DA desktop, the customer contactcomprising an inquiry and a response to the inquiry; analyzing thecustomer contact in the at least one STAS unit according to at least onecontact analytics rule to determine the content of the inquiry and theresponse; analyzing the content of the inquiry and the response with theDAE to calculate a similarity score from the inquiry and the response;storing the inquiry and the response in a corresponding inquiry-responsesub-database in the inquiry-response database based upon the similarityscore; analyzing at least one of the inquiry-response sub-databases inthe DAE according to at least one inquiry analytics rule to determinethe common response for that inquiry-response sub-database; andassigning at least one common inquiry-response (CIR) knowledge documentto the analyzed inquiry-response sub-database.
 2. The method of claim 1,further comprising repeating receiving at least one customer contactinto the at least one DA desktop, analyzing the customer contact in theat least one STAS unit according to at least one contact analytics ruleto determine the content of the inquiry and the response, analyzing thecontent of the inquiry and the response with the DAE to calculate asimilarity score from the inquiry and the response, and storing theinquiry and the response in a corresponding inquiry-responsesub-database in an inquiry-response database until the inquiry-responsedatabase exceeds a data volume threshold for at least oneinquiry-response sub-database.
 3. The method of claim 1, furthercomprising reassigning the CIR knowledge document after a set period. 4.The method of claim 1, further comprising detecting a shift in inquiriesand responses to the inquiries beyond a preset threshold, andreassigning the CIR knowledge document.
 5. The method of claim 1,further comprising reassigning the CIR knowledge document upon change tothe at least one contact analytics rule or the at least one inquiryanalytics rule.
 6. The method of claim 1, further comprising manuallyreassigning the CIR knowledge document.
 7. The method of claim 1,further comprising intercepting an incoming customer contact andanalyzing it in real time with the STAS unit to determine at least onekeyword or intent.
 8. The method of claim 7, further comprisingcommunicating the at least one keyword or intent to the DAE anddetermining if any CIR knowledge documents are applicable by analyzingthe at least one keyword or intent with the DAE to calculate asimilarity score.
 9. The method of claim 8, further comprisingtransmitting any CIR knowledge documents that are applicable to the DAdesktop.
 10. The method of claim 1, further comprising intercepting anoutgoing customer contact and determining in real time if an author ofthe outgoing customer contact correctly used a CIR knowledge document byanalyzing the outgoing customer contact with the DAE to calculate asimilarity score.
 11. The method of claim 10, further comprisingblocking the outgoing customer contact if the similarity score is belowa low threshold.
 12. The method of claim 10, further comprising copyingthe outgoing customer contact and flagging the outgoing customer contactfor future review if the similarity score is below a low threshold. 13.The method of claim 10, further comprising rerouting the outgoingcustomer contact to the DA desktop for review if the similarity score isbelow a low threshold.
 14. The method of claim 1, further comprisingperforming an analysis of at least one common inquiry topic, at leastone trend in inquiry topics, or at least one user's use of CIR knowledgedocuments, wherein the analysis is performed using the DAE.
 15. Themethod of claim 14, further comprising recommending at least one of anupdate to existing templates and/or scripts, a modification to customerservice representative (CSR) training, or retraining a CSR.
 16. Themethod of claim 14, further comprising updating publicly availableinformation.
 17. A customer engagement center (CEC) system fordeveloping a common inquiry response, comprising: a dynamic analysis(DA) system, comprising: a DA processor; and a non-transitory computerreadable medium programmed with computer readable code that uponexecution by the DA processor causes the DA processor to execute amethod for real-time predictive scheduling, comprising: receiving atleast one customer contact, the customer contact comprising an inquiryand a response to the inquiry; analyzing the content of the inquiry andthe response with the DAE to calculate a similarity score from theinquiry and the response; storing the inquiry and the response in acorresponding inquiry-response sub-database in the inquiry-responsedatabase based upon the similarity score; analyzing at least one of theinquiry-response sub-databases according to at least one inquiryanalytics rule to determine the common response for thatinquiry-response sub-database; and assigning at least one commoninquiry-response (CIR) knowledge document to the analyzedinquiry-response sub-database.
 18. The CEC system of claim 17, whereineach set of related inquiries and corresponding responses has a separateinquiry-response sub-database.
 19. The CEC system of claim 17, whereinthe inquiry-response database exceeds a data volume threshold in atleast one inquiry-response sub-database.
 20. A non-transitory computerreadable medium programmed with computer readable code that uponexecution by a dynamic analysis (DA) processor causes the DA processorto execute a method for developing a common inquiry response on acustomer engagement center (CEC) system, comprising: receiving at leastone customer contact, the customer contact comprising an inquiry and aresponse to the inquiry; analyzing the content of the inquiry and theresponse with the DAE to calculate a similarity score from the inquiryand the response; storing the inquiry and the response in acorresponding inquiry-response sub-database in the inquiry-responsedatabase based upon the similarity score; analyzing at least one of theinquiry-response sub-databases according to at least one inquiryanalytics rule to determine the common response for thatinquiry-response sub-database; and assigning at least one commoninquiry-response (CIR) knowledge document to the analyzedinquiry-response sub-database.